Synthetic aperture radar (SAR) acquires raw radar data from target objects or terrain typically using a space-based platform. Post-processing converts the raw SAR data to an amplitude image and phase maps, such that objects and terrain can be visualized. Due to the very high computational complexity, the SAR data processing is typically performed on the ground.
FIG. 1 shows a conventional SAR system. Raw SAR data 101 are acquired by a space-based platform 110, e.g., a satellite, space station or shuttle. The raw data are transmitted 110 to a ground station for post-processing 120 to obtain a SAR image 130.
Alternatively as shown in FIG. 2, compression 151 is applied to the raw SAR data 101 before transmission 120. Then, the compressed raw data are transmitted to the ground station, where the data are decompressed 152 and processed 120 to produce the SAR image 130. A high coding efficiency is required to reduce the bandwidth of transmission, while at the same time enabling a high quality reconstruction of the SAR image 130 from the compressed data.
It is known that standardized image compression methods, such as JPEG, are not suited for achieving high coding efficiency of raw SAR data. The main reason is due to the noisy nature of the raw SAR data, which is not well matched with the JPEG coding standard that has been optimized for coding natural images. It is noted that the statistics of raw SAR data, which resemble Gaussian noise, are completely different from statistics of natural image data.
Block adaptive quantization (BAQ) can be used to compress the raw SAR data acquired by the Magellan spacecraft, see e.g., U.S. Pat. No. 5,661,477, “Methods for compressing and decompressing raw digital SAR data and devices for executing them,” and Kwok et al., “Block Adaptive Quantization of Magellan SAR Data,” IEEE Trans on Geosc. and Remote Sensing, vol. 27, No. 4, pp. 375-383, July 1989, all incorporated herein by reference. Similar to many conventional image compression methods, BAQ quantizes raw pixel values, e.g. from 8 bits to 4 bits. However, for inputs with a large dynamic range, such as raw data acquired by SAR, the quantization step size has to be adaptive to the level of input signals in order to achieve more efficient compression. Therefore, BAQ adapts the quantization step size for each 16×16 block.
To achieve a higher resolution, the sampling rate of the raw data needs to increase. Given limitation in transmission bandwidth, a higher compression ratio is also needed, while maintaining the quality of the SAR image. BAQ could provide reasonably quality SAR images at moderate compression ratios. However, it is known that the quality degrades substantially when the compression ratio is greater than 2:1. Hence, there is a need to provide a method for compressing raw SAR at an increased compression ratio without decreasing quality.
A number of methods are known for compressing processed SAR data, but relatively few methods have been developed for compressing raw SAR data, see Delp et al., “Image Compression Using Block Truncation Coding (BTC)”, IEEE Transactions Communications, Vol. Com-27, No. 9, September 1979, Magli et al., “Wavelet-based compression of SAR raw data,” Geoscience and Remote Sensing Symposium, 2002, IEEE International, pp. 1129-1131, and “Frequency domain raw SAR data compression for multi-mode SAR Instruments,” National Aerospace Laboratory (NLR), EUSAR 2006, 16-18 May 2006, all incorporated herein by reference.
The BTC method uses a two-level quantizer that adapts to local block properties of the SAR image. That method has a low complexity, and can achieve a high compression ratio, e.g., about 4:1. However, the quality of the output image is substantially degraded. In general, BTC performs worse than BAQ.
Magli et al. apply adaptive quantization to blocks of pixels, as in BAQ. A digital wavelet transform (DWT) is first applied to the raw data, such that the BAQ is performed on wavelet coefficients in the frequency domain. It is well known that the wavelet transform has a high computational complexity relative to block-based scheme and also requires significantly more memory. Thus, that method is not suitable for low complexity devices.
The NLR (FFT-ECBAQ) method applies a 2-dimensional fast Fourier transform (FFT) to the raw SAR data, and then an entropy-constrained BAQ (ECBAQ), followed by entropy encoding. The major drawback of that method is the FFT causes cross-leak noise with smaller block sizes, which implies that larger FFT-sizes are preferred, e.g., an FFT size of 128×64. However, that FFT is too complex to implement in low complexity devices. In fact, that method relies on the fastest known FFT-oriented DSP ASIC (powerFFT™) to compute the long sized FFT. Without that chip, the transform size would have to be greatly reduced, to e.g., by a factor of 8 or 16. The maximum throughput of that method is up to 125 mega-samples per second (MSPS), or 1 Gbits per second, assuming each sample is 8 bits, which is not sufficient for applications requiring higher throughputs, e.g. 4 Gbits per second, as required by applications that aim to reconstruct high resolution SAR images.
It is desirable to have a method and apparatus for compressing raw SAR data, which can achieve an increased compression ratio, high resolution SAR images, while maintaining low hardware complexity, and overcome the disadvantages of the prior art.